Comparing the Efficiency of Software-Based Speech Recognition Versus Traditional Telephone Transcription in an Outpatient Physical Medicine and Rehabilitation Practice
Bibliographic record
Abstract
INTRODUCTION: Speech recognition (SR) uses computerized word recognition software that automatically transcribes spoken words to written text. Some studies indicate that SR may improve efficiency of electronic charting as well as associated cost and turnaround time1,2, but it remains unclear in the literature whether SR is superior to traditional transcription (TT). This study compared the impact of report generation efficiency of SR to TT at the Canadian Armed Forces Health Services Centre. MATERIALS AND METHODS: Dragon Medical Dictation™ SR software and traditional telephone dictation TT were used for two prespecified clinical days per week. In order to adjust for note length, total transcription efficacy was calculated as follows: word count/[dictation time + correction time]. The means and standard deviations were then separately calculated for TT visits and for SR visits. Differences in transcription efficacy and in visit measures, including patient demographics, visit duration, number of issues raised during the visit, and interventions performed, were compared using ANOVA, with the significance level set to 0.05. RESULTS: A total of 340 consecutive visits were analyzed; 198 were dictated over the phone using TT and 142 were transcribed using SR software. Dictation efficacy was significantly higher (p < 0.0001) for TT as compared to SR, while turnaround times were shorter for SR (0.12 versus 4.75 days). CONCLUSIONS: In light of these results, the Canadian Forces Health Services Centre in Ottawa has returned to use of TT because the relative inefficiency of report generation was deemed to have a greater impact on clinical care when compared to slower dictation turnaround time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".